The term “computer-based search” (or just “search”) as used herein, refers to the search of any machine-accessible data using a computer. The term “search engine,” as used herein, refers to any system that can perform a computer-based search. A specification of what a search engine searches for can be referred to herein as a “query” and the result, produced by the search engine, can be referred to herein as a “search result.”
The utility of computer-based search is well-known and many types of search engines are available. A particularly well-known category of computer-based search can be referred to herein as “keyword-based search.” In keyword-based search, the search engine accepts a query that includes at least one keyword and, with the at least one keyword, searches an indexed database. A well known example search engine, for keyword-based search, is provided by GOOGLE of Mountain View, Calif., U.S.A. A large percentage of World-Wide Web pages are accessible via the GOOGLE indexed database.
Keyword search is most effective when records, that are likely to be of interest to the user, can be located with terms that are highly specific to the topic of interest. In many instances, however, highly specific keyword terms can only partly describe the topic of interest. The problems resulting from this inability of keywords, to more fully describe certain search topics, can be twofold. First, a set of records can be returned that is too large for the user to review in a reasonable amount of time. Second, the set of records returned can include many records that are off-topic.
GOOGLE attempts to address the limitations of keywords by ranking the records (more specifically, the web pages) returned according to a “popularity” metric. According to GOOGLE, the popularity of a web page is proportional to the number of other web pages that point to it.
However, for many types of search topics, popularity is not an acceptable proxy for the portion of the topic that could not be adequately expressed with keywords.
An example kind of search, where popularity is often not an acceptable proxy, is called “technology scouting.” In technology scouting, the user of a search engine is looking for an existing technology (“ET1”) that can address (or solve) his or her problem (“P1”). To accomplish technology scouting, one would like to search a large portion of the Internet for that content where something (in some cases, an existing technology) is discussed as part of a solution to P1. Unfortunately, it can be difficult or impossible to express, with keywords, the requirement that certain content express the concept of “solving a problem.”
It would therefore be desirable to be able to retrieve records not only on the basis of keywords, but also on the basis of whether a record expresses a concept, such as the concept of “solving a problem.”
Regardless of the particular search engine by which a search result is produced, there is often a need for a post-search analysis tool by which the search result can be more effectively or easily evaluated. A post-search analysis tool can be used to re-organize a search result into a form where the information, which is of interest to the user, is more readily accessible.
For example, in the case of technology scouting, a user would likely prefer search results organized according to potential solutions (e.g., existing technologies), for the problem sought to be addressed (e.g., a problem P1).
Thus, there is a need for post-search analysis tools that enable a user to more efficiently evaluate a search result.